Pass speaker id during verification

src/python/larynx_train/vits/dataset.py

@@ -98,7 +98,8 @@ class LarynxDataset(Dataset):
 
     @staticmethod
     def load_dataset(
-        dataset_path: Path, max_phoneme_ids: Optional[int] = None,
+        dataset_path: Path,
+        max_phoneme_ids: Optional[int] = None,
     ) -> Iterable[Utterance]:
         num_skipped = 0
 
@@ -118,7 +119,10 @@ class LarynxDataset(Dataset):
                         num_skipped += 1
                 except Exception:
                     _LOGGER.exception(
-                        "Error on line %s of %s: %s", line_idx + 1, dataset_path, line,
+                        "Error on line %s of %s: %s",
+                        line_idx + 1,
+                        dataset_path,
+                        line,
                     )
 
         if num_skipped > 0:

src/python/larynx_train/vits/lightning.py

@@ -25,7 +25,11 @@ class VitsModel(pl.LightningModule):
         # audio
         resblock="2",
         resblock_kernel_sizes=(3, 5, 7),
-        resblock_dilation_sizes=((1, 2), (2, 6), (3, 12),),
+        resblock_dilation_sizes=(
+            (1, 2),
+            (2, 6),
+            (3, 12),
+        ),
         upsample_rates=(8, 8, 4),
         upsample_initial_channel=256,
         upsample_kernel_sizes=(16, 16, 8),
@@ -215,7 +219,9 @@ class VitsModel(pl.LightningModule):
             self.hparams.mel_fmax,
         )
         y_mel = slice_segments(
-            mel, ids_slice, self.hparams.segment_size // self.hparams.hop_length,
+            mel,
+            ids_slice,
+            self.hparams.segment_size // self.hparams.hop_length,
         )
         y_hat_mel = mel_spectrogram_torch(
             y_hat.squeeze(1),
@@ -228,7 +234,9 @@ class VitsModel(pl.LightningModule):
             self.hparams.mel_fmax,
         )
         y = slice_segments(
-            y, ids_slice * self.hparams.hop_length, self.hparams.segment_size,
+            y,
+            ids_slice * self.hparams.hop_length,
+            self.hparams.segment_size,
         )  # slice
 
         # Save for training_step_d
@@ -276,7 +284,12 @@ class VitsModel(pl.LightningModule):
             text = test_utt.phoneme_ids.unsqueeze(0).to(self.device)
             text_lengths = torch.LongTensor([len(test_utt.phoneme_ids)]).to(self.device)
             scales = [0.667, 1.0, 0.8]
-            test_audio = self(text, text_lengths, scales).detach()
+            sid = (
+                test_utt.speaker_id.to(self.device)
+                if test_utt.speaker_id is not None
+                else None
+            )
+            test_audio = self(text, text_lengths, scales, sid=sid).detach()
 
             # Scale to make louder in [-1, 1]
             test_audio = test_audio * (1.0 / max(0.01, abs(test_audio.max())))

src/python/larynx_train/vits/models.py

@@ -686,6 +686,7 @@ class SynthesizerTrn(nn.Module):
     ):
         x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
         if self.n_speakers > 1:
+            assert sid is not None, "Missing speaker id"
             g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
         else:
             g = None